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9Initial conceptual framework and literature review for understanding adaptive policies
2C H A P T E R
Initial conceptual framework and literature review forunderstanding adaptive policies
2.1 Concepts: initial terminology and framework
2.1.1 Background terms and concepts
2.1.2 Our initial conceptual framework for adaptive policies
2.2 Literature review
2.2.1 Insights from the adaptive policy-making literature
2.2.2 Insights from adaptive management literature
2.2.3 Insights from policy pilot studies
2.2.4 Insights from the policy learning and change literature
2.2.5 Insights from the institutional learning literature
2.2.6 Insights from natural systems
2.2.7 Insights from complex adaptive systems theory
10 IISD-TERI-IDRC Adaptive Policies Project
11Initial conceptual framework and literature review for understanding adaptive policies
1 International Institute for Sustainable Development, Canada2 The Energy and Resources Institute, India3 Adaptive Resource Management Ltd, Canada
C H A P T E R
Initial conceptual framework andliterature review for understanding
adaptive policiesDarren Swanson1 , Henry David Venema1, Stephen Barg1, Stephen Tyler3,
John Drexhage1, Preety Bhandari2 , Ulka Kelkar2
2
What do we mean by adaptive policies? One can imagine, in one case, a policy framework
that needs little adjustment to cope with new conditions. Another case would be one in
which the core elements of policies may persist under stress, but the ways in which policy
is implemented must change in order to meet unexpected conditions. We also want to consider cir-
cumstances in which policies are fundamentally untenable due to changing external conditions and
must be completely overhauled. Policies in these circumstances will have exceeded their adaptive
capacity, but an adaptive policy should quickly learn how to diagnose and respond to the new context.
This section presents our initial conceptual framework for understanding adaptive policies and
policy-making based on insights from the relevant literature.
2.1 Concepts: initial terminology and frameworkWhile the literature relating directly to the topic of adaptive policies is limited, there exist other useful
sources of information within the fields of societal and policy learning, adaptive management for
natural resources, and complex adaptive systems research. The key insights gleaned from this infor-
mation are discussed below as a framework for an initial understanding of adaptive policies for this
project.
2.1.1 Background terms and concepts
Before presenting a conceptual framework for adaptive policies, it is necessary to define a few basic
terms and concepts. First, a policy can be thought of as a broad statement of purpose and process
for addressing a particular social, economic, or environmental issue. The intent of a policy is imple-
mented via policy instruments such as regulatory, economic, expenditure, and institutional instru-
ments (Box 2.1).
12 IISD-TERI-IDRC Adaptive Policies Project
Box
2.1
For purposes of this report, any policy instrument could be considered to be made up of two
components.
P Design defining how the instrument is designed to perform
P Implementation actions of the people and organizations which implement the rules of the
policy instrument
These two components are illustrated in Figure 2.1, which presents an idealized process of policy
design and implementation. Policies are designed with varying degrees of consultation with relevant
stakeholders, and it is typically the case that an institution or organization different from the one,
which designed the policy, is responsible for implementing the policy.
One typology of policy instruments
Policy instruments can be placed under four broad categories (IISD and
TERI 2003).
Economic instruments refer to measures that directly influence the price that a
producer or consumer pays for a product or activity. Economic instruments also include
market-based instruments or financial incentives. Specific economic instruments include
tradable permits, deposit refunds, performance bonds, taxes, user fees, subsidies, tax breaks,
earmarked taxes and funds, and administered prices.
Regarding direct expenditure instruments, governments influence producer and
consumer behaviour through channelling expenditures directly at the behaviour they want to
encourage (Barg, Boame, Brown, et al. 2003). This category of instruments is characterized
by broad programmes of expenditures targeted at a macro level to foster activities such as
technological innovation. Specific instruments of direct expenditure include the very wide
range of programme, expenditures that governments make as well as some particular cases
like green procurement, research and development, and moral suasion.
Regulatory instruments describe efforts to create change via legal avenues.
Several different regulatory instruments fall under this category including legislation, liability,
enforcement activity, and competition and deregulation policy instruments. Legislative
instruments involve the acts and regulations that a government passes to create a legal
mandate for change. Enforcement instruments are considered separately in that there could
be a legislative requirement and no enforcement—the combination of course, leads to an
ineffective legislative instrument. These instruments aim to induce socially responsible
behaviour by establishing legal liability for certain activities such as natural resource damage,
environmental damage, property damage, damage to human health, non-compliance with
environmental laws and regulations, and non-payment of due taxes, fees, or charges
(Panayotou 1998, p. 41).
Institutional instruments affect the workings of the government itself in an effort to
promote change. Included in this category are internal education efforts, and internal policies
and procedures. Efforts such as the National Round Table on the Environment and the
Economy in Canada are often initiated to educate government decision-makers and the policy
community on issues. Internal policies and procedures act to change the way governments go
about making and implementing decisions. For example, Canada’s Office of the Auditor
General includes a Commissioner of Environment and Sustainable Development with a
mandate of overseeing the drafting of sustainable development strategies by all federal
governmental departments.
13Initial conceptual framework and literature review for understanding adaptive policies
Consider, for example, a law for automobile speed limits. Penalties are defined by government
policy- and law-makers for drivers who exceed the speed limit. The policy is implemented by a police
officer who stops the speeding driver. The police officer then has discretion on how to implement the
policy. Depending on the actual speed of the driver and the road conditions at the time, the officer will
decide on a warning or issue a speeding ticket. The police department may decide that speeding is an
issue that will be given low enforcement priority.
Thinking of policy and implementation as two distinct yet integrated processes is helpful from a
practical perspective. Pritchett and Woolcock (2004) suggest that much more attention has to be paid
to the ‘implementation’ cycle. Therefore, in relation to this research project, designing adaptive and
shared learning mechanisms among practitioners through that cycle, not just feedback to policy, is
important. They argue that design and delivery of key, transaction-intensive public services can be
described using two characteristics: the degree of discretion involved in decisions, and the intensity
of transactions required. In a western context, policy design, for example, requires high discretion, but
limited transactions; whereas, policy implementation requires many transactions, but little discretion.
Pritchett and Woolcock (2004) argue that in developing countries, policy implementation has more
discretion relative to the western context given a heavy dependence on personal context (context-
specific, depending on tribe, caste, and so on), connections, and experience. In such a case, generalized
rules and procedures are problematic.
2.1.2 Our initial conceptual framework for adaptive policies
Our conceptual framework of adaptive policies is illustrated in Figure 2.2. Adaptive policies have
features that enable them to continue to adapt to both anticipated and unanticipated conditions.
In dealing with a range of anticipated conditions, adaptive policies make use of no-regrets alternatives
and employ mechanistic adjustments triggered by the monitoring of key system indicators. Beyond
these more conventional features, adaptive policies incorporate scheduled evaluations to respond to
unanticipated circumstances. Perhaps most important, the design and implementation of adaptive
policies is informed by complex adaptive systems theory. This enables policies to self-adjust, and be
better suited to the infinitely complex and dynamic interactions among people, the economy, and
environment.
The conceptual framework is based largely on three literature sources, which used the terminology
of adaptive policy explicitly4 and a host of literature related to effective policy interventions in com-
plex adaptive systems. The specific insights for the conceptual framework gained from this literature
are described in the sections that follow.
4 One in relation to the transportation sector in the Netherlands (Walker, Rahman, and Cave 2001), one related to adaptive management
in the Columbia River Basin in the United States (Lee 1993), and the other dealing with agent-based modelling (Bankes 2002).
Figure 2.1 Idealized illustration of policy design and implementation
14 IISD-TERI-IDRC Adaptive Policies Project
Figure 2.2 Initial conceptual framework for adaptive policies
Box
2.3
International carbon trading market: an example of policy adaptability
Tradable permits are one type of economic instrument, a recent example of which is
the international market for carbon credits created by the Kyoto Protocol, which in
2005 traded 397 MT (million tonnes) of CO2
(carbon dioxide) at an average price of 6.7 euros
per tonne of CO2.
Markets have adaptive features in that they provide signals that reflect changing (demand
and supply) conditions, while leaving specific decisions up to the individual, company, or
government. For instance, the price of carbon reflects the availability of carbon credits from
various countries, the relative cost of reducing emissions from different types of projects and
mechanisms, and demand determinants as diverse as the weather, oil prices, and risk
perceptions. Companies can choose to continue to emit by purchasing credits at the prevailing
price, or invest in energy efficiency and carbon abatement technologies.
Thus, the decision can be made by taking many policy, technical, and market factors into
account, and as those conditions change, the decision-maker can respond flexibly.
Box
2.2
The Canadian income tax system as an example of policy robustness
One example of an economic instrument is the income tax system, both personal
and corporate. In Canada this system is complex with legislation, regulations, and
government policies covering many thousands of pages. The system is administered
by two separate departments of the federal government (Finance makes the policy, and the
Canadian Revenue Agency operates the assessment and collection system), and even has its
own court (the Tax Court of Canada).
In Canada, individuals are required to pay a government tax equal to a certain percentage
of their yearly income, but not everyone pays the same percentage. Individuals with lower
incomes pay a smaller percentage of their total income to tax compared to those with higher
incomes. These are called tax brackets. This is to acknowledge that the lower is your total
income, the greater the burden of paying a dollar of tax (for example, more difficult to afford
subsistence items such as food, shelter, and transportation).
15Initial conceptual framework and literature review for understanding adaptive policies
Box
2.4
Dealing with anticipated conditions
P Adaptive policies are ‘devised not to be optimal for a best estimate future, but robust across a
range of futures (Walker, Rahman, and Cave 2001)’.
P Adaptive policies ‘need to be evaluated on their robustness properties, not on their performance
on any single case (Bankes 2002)’.
P Adaptive policies ‘respond to changes over time and make explicit provision for learning (Walker,
Rahman, and Cave 2001)’.
P Adaptive policies make ‘adaptation explicit at the outset of policy formulation. Thus, the inevitable
policy changes become part of a larger, recognized process and are not forced to be made repeat-
edly on an ad hoc basis (Walker, Rahman, and Cave 2001)’.
How might policy be designed to perform in a range of anticipated circumstances? This ability
depends largely on a good understanding of cause and effect relationships. In practice this can be
accomplished either through a no-regrets policy (that is, works well in a range of anticipated condi-
tions without a modification to the original policy design) or through mechanistic adjustment trig-
gered by a monitoring process.
Pertaining to no-regrets policies, Bankes (2002) suggests that computers can be used to ‘discover
policies that are robust across multiple scenarios or alternative models, and to identify and graphically
depict sets of policies with satisfactory robustness’. He concludes that computers can be used to ‘…
find important scenarios by searching through such ensembles, in particular to find cases that break a
proposed policy. Such worst cases can stimulate users to modify the range of possible policies to allow
for combinations that hedge against these possibilities’.
Automatic stabilizers as an example of policy adaptability
A set of expenditure instruments that exhibit adaptive characteristics is that of
‘automatic stabilizers’. These are expenditure instruments that operate on the
opposite cycle to the economy as a whole: when the economy is growing, automatic stabilizers
spend less money, and when the economy is shrinking, they spend more. The net result is that
the expenditures take place at a time when they will help the economy out of a downturn.
One standard example is unemployment insurance, which pays out to people who become
unemployed.
In a paper reviewing Canada’s UI (Unemployment Insurance) system, Dungan, Peter,
and Murphy (1995) found that the UI policy instrument had a clear stabilizing effect on the
Canadian economy. In the context of unemployment insurance, Dungan, Peter, and Murphy
(1995) describe the automatic stabilization function as
‘It takes time before the problem of rising unemployment or a sluggish
economy is recognized. Because there is a further lapse of time before policy
decisions are made, implemented, and have an effect on the economy, economists
and policy-makers look for ‘automatic stabilizers’ that respond immediately when
the economy slips from the level of full employment. Such automatic stabilizers
should respond quickly – changing taxes, or increasing or reducing government
spending – to even out the economic impacts of cyclical fluctuations.
There are two features of the UI system that make it an automatic stabilizer.
First, when unemployment increases, total UI payments increase, with only a
short time lag. Secondly, when people lose their jobs, they and their employers
immediately stop paying the UI premiums associated with those jobs. When an
economic downturn results in fewer jobs, the total tax represented in UI
premiums immediately falls. At the same time, increased payments in UI benefits
put some purchasing power back into the economy by automatically increasing
government spending.’
16 IISD-TERI-IDRC Adaptive Policies Project
We saw from the policy instrument examples that in the case of the Income Tax Act of Canada,
and for most income tax systems in fact, tax brackets exist to allow the level of tax collected to be fairly
distributed according the level of one’s own income. This is a level of adaptability that is built into the
policy instrument directly.
But a policy need not be static over time. When monitoring reveals that conditions have changed
and design assumptions invalidated, this can trigger an improvement in the policy.
From the definitions of Walker, Rahman, and Cave (2001) it is clear that from a pragmatic
perspective, aspects of indicators and assessment play an important role in adaptive policies.
Of particular relevance are their ideas related to the following.
P Signposts – information that should be tracked in order to determine whether defensive or correc-
tive actions, or a policy reassessment is needed
P Triggers – critical values of the signpost variables that lead to implementation of corrective
actions
The literature on policy pilots (Section 2.3.2) informs us that aspects of measurement and assess-
ment need to focus on both impact (to test the likely effects of policies) and process (to explore the
practicalities of implementing a policy in a particular way) (UK 2003).
A better understanding of cause–effect relationships can inform both no-regrets policy design,
and the design of triggers and corrective actions. Insights from efforts to improve health policies in
cities in Canada (Glouberman, Campsie, Gemar, et al. 2003) remind us that understanding local
conditions is vital to interacting in complex adaptive systems and that not doing so can make condi-
tions worse due to the many inherent interdependencies. Perhaps most importantly, Glouberman,
Campsie, Gemar, et al. (2003) recommend respecting historical conditions since complex adaptive
systems are ‘shaped by their past, and a knowledge of this history may suggest constraints on and
opportunities on what can be done in the future’.
Dealing with unanticipated conditions
Adaptive policies are ‘designed from the outset to test clearly formulated hypotheses about the
behaviour of an ecosystem being changed by human use’ (Lee 1993).
There appear to exist at least two means by which a policy can be made adaptive in the face of
surprises and slower seemingly undetectable change. The first is discussed by Walker, Rahman, and
Cave (2001), Lee (1993), and others, and is built on the ideas of explicit review and reassessment of
policy. A second means is by adhering to principles for effective intervention in complex adaptive
systems.
Walker, Rahman, and Cave (2001) in addition to recommending corrective actions in response
to triggers, also describe ‘defensive actions’ which they note are taken after the fact to preserve a
policy’s benefits, and ‘reassessment’ of the instrument when the policy has lost validity.
As illustrated in Figure 2.1, the learning and improvement could occur in both policy design and
implementation. For example, the institution, which designed the policy might have an internal
process of checking whether the intended societal change is actually occurring. If this institution has
the discretion to change instrument rules, such monitoring, evaluation, and learning might directly
result in an improvement in the instrument design, which would then need to be communicated to
those responsible for implementing the policy.
Similarly, the people and organizations responsible for implementing a policy instrument might
learn of a necessary change required to adequately implement the policy. If they have the discretion
to make this change, they can do so, otherwise they would communicate this learning to the policy
designers with the hope that this learning results in an improvement to the instrument delivery
system. It may also be the case that the implementing institution, being close to the ground, learns
that the desired societal change is not occurring, or that something negative and unexpected is occur-
ring as a result of the instrument design. If the implementing institution does not have the discretion
to change the design, this learning would need to be communicated to the policy designers in order for
the necessary policy improvement to be assessed and carried out.
17Initial conceptual framework and literature review for understanding adaptive policies
Another possible means by which policies can become adaptive is by adhering to certain design
principles, which make a policy better suited to complex adaptive systems. In the policy and manage-
ment fields, a substantive body of knowledge has emerged over the last 10 years on the topic of com-
plex adaptive systems. In all cases the study of complex adaptive systems was based on a need to
better see the structures underlying complex situations and better identify leverage points for change.
The recent study and application of complex adaptive systems can be seen in many fields including
business management, health care, information technology, transportation, sustainable development,
and international development.
Table 2.1 lists a set of principles for effective policy intervention compiled from the literature
sources mentioned in Section 2.2. These principles have been organized according to an idealized
policy cycle to help make these principles more pragmatic for policy-makers. The table also lists which
aspect of policy flexibility the principle contributes the most to (for example, to adaptability or robust-
ness).
For example, one of the principles that is frequently mentioned is the capacity of systems to self
organize. This suggests that one way to make an instrument more adaptive is to decentralize the
decision-making as much as is possible, allowing it (at least potentially) to respond to local circum-
stances. As we proceed with the case studies, we will examine this possibility.
Additionally, the literature on complex adaptive systems was particularly insightful in under-
standing the rationale for adaptability through learning and improvement. For example, Glouberman,
Campsie, Gemar, et al. (2003) recognized that in complex adaptive systems policies ‘undergo selection
by the system’ and therefore, it is important to include ‘evaluating performance of potential solutions,
and selecting the best candidates for further support and development’. Additionally, they also note
the importance of fine-tuning policies because ‘in complex adaptive systems, which change over time
and respond dynamically to outside forces, it is necessary to constantly refine interventions through a
continual process of variation and selection’.
A more detailed description of the literature underpinning Table 2.1 is provided in
section 2.2.7.
2.2 Literature reviewThis section provides a more in depth look at the literature, which informed the initial terminology
and framework presented in Section 2.1. A broad literature was conducted to explore concepts of
relevance for advancing an initial understanding of adaptive policies in the context of this project.
We begin in this section by focusing on literature, which explicitly mentions the terminology of adap-
tive policies or policy-making. The literature review necessarily engages in some divergent thinking in
order to develop some peripheral vision, to look for insights on adaptive policy-making from sources
of literature, which we perceived to be related to the concept of adaptive policy-making.
2.2.1 Insights from the adaptive policy-making literature
Some of the first hints towards adaptive policy-making actually came early in the 1900s. Dewey (1927)
put forth an argument proposing that ‘policies be treated as experiments, with the aim of promoting
continual learning and adaptation in response to experience over time’ (Busenburg 2001). Over
60 years later Kai Lee appears to be one of the first to use the term ‘adaptive policy’ in his account of
integrating science and politics in the highly contested issue of salmon fisheries restoration and
hydropower development in the pacific northwest of the United States. Lee describes adaptive policy
as a policy that is ‘designed from the outset to test clearly formulated hypotheses about the behaviour
of an ecosystem being changed by human use’ (Lee 1993).
Walker and Marchau (2003) in a special issue of the international journal Integrated Assessment
give direct focus to the terms adaptive policies, policy analysis, and policy-making, and take them to a
more pragmatic level. They suggest that policies be ‘adaptive—devised not to be optimal for a best
estimate future, but robust across a range of futures’. They go on to describe that such policies ‘should
combine actions that are time urgent with those that make important commitments to shape the
future and those that preserve the needed flexibility for the future’. Their notion of adaptive policies is
18 IISD-TERI-IDRC Adaptive Policies Project
Idealizedpolicy cycle
Understandingthe issue
Policyobjectivesetting
Policy designandimplementation
Policymonitoring andevaluation
Policy learningand adaptation
Table 2.1 Policy design and implementation insights from the complex adaptive systems literature
Principles for policy-making in settings characterized by surprise, long-term change, anduncertainty
P Understand local conditions, strengths, and assets (Glouberman, Campsie, Gemar,et al. 2003)
P Respect history: ‘adaptive systems are shaped by their past and a knowledge of thishistory may suggest constraints on and opportunities on what can be done in the future.’(Glouberman, Campsie, Gemar, et al. 2003)
P Understand interactions with the natural, built, and social environment (Glouberman,Campsie, Gemar, et al. 2003; Holling 1978)
P Look for short-term finer-grained criteria of success that can usually stand in for longer-run broader goals (Axelrod and Cohen 2000)
P Gather multiple perspectives from a range of stakeholders involved in the issue(Holling 1978)
P Create opportunity for self-organization, and build networks of reciprocal interaction thatfoster trust and cooperation (Berkes, Colding, and Folke 2003; Glouberman, Campsie,Gemar, et al. 2003; Axelrod and Cohen 2000)
P Clear identification of the appropriate spatial and temporal scale is vital to integratedmanagement (the ecosystem approach; Shepherd 2004).
P Ensure that social capital remains intact (Ruitenbeek and Cartier 2001)P Promote effective neighbourhoods of adaptive cooperation (Axelrod and Cohen 2000)P Promote variation and redundancy (Berkes, Colding, and Folke 2003; Glouberman,
Campsie, Gemar, et al. 2003; Holling 1978)P Balance exploitation of existing ideas and strategies, and exploration of new ideas
(Axelrod and Cohen 2000)P Facilitate copying of successes (Ruitenbeek and Cartier 2001; Axelrod and Cohen
2000).P Use social criteria to support the growth and spread of valued criteria (Axelrod and
Cohen 2000)P Combine experiential and experimental knowledge (Berkes, Colding, and Folke 2003)P Nurture, and enhance social and ecological memory (Berkes, Colding, and Folke 2003)P Build adaptive capacity (Berkes, Colding, and Folke 2003)P Place effort on determining significant connections rather than attempting to measure
everything (Holling 1978)P Increase information on unknown or partially unknown social, economic, and
environmental effects (Holling 1978)
P Integral to design are the monitoring and remedial mechanisms—should not be post adhoc additions after implementation (Holling 1978)
P Conduct selection: ‘In complex adaptive systems possible solutions undergo selectionby the system. It is, therefore, important to include ‘evaluating performance of potentialsolutions, and selecting the best candidates for further support and development.’(Glouberman, Campsie, Gemar, et al. 2003)
P Assess strategies in light of how consequences spread: look for linkages in unusualplaces (Axelrod and Cohen 2000)
P Fine-tune the process: ‘in complex systems, which change over time and responddynamically to outside forces, it is necessary to constantly refine interventions through acontinual process of variation and selection’ (Glouberman, Campsie, Gemar,et al. 2003).
P Understand carefully the attribution of credit: ‘A common mistake in complex systems isto assign blame or credit to a small part of the system, when in fact the entire system isresponsible; one of the most important elements of any policy discussion is the specificincentives facing individual agents’ (Axelrod and Cohen 2000).
19Initial conceptual framework and literature review for understanding adaptive policies
that they are those policies ‘that respond to changes over time and that make explicit provision for
learning’. This approach requires that learning and adaptation of the policy be made ‘explicit at the
outset and the inevitable policy changes become part of a larger, recognized process, and are not
forced to be made repeatedly on an ad hoc basis’ (Walker and Marchau 2003).
The adaptive policy-making process as articulated by Walker, Rahman, and Cave (2001) begins
with stage setting and assembling basic policy steps, while the remaining parts articulate the critical
learning loop processes (Figure 2.3). Some of the innovative steps of their adaptive policy-making
process include the following.
P Separate actions now from those that can or should be deferred until more information becomes
available
P Develop indicators such as signposts for monitoring changes and identify thresholds or triggers
for contingency plans
P Establish limits to the validity of the analysis, that once violated, should lead to reassessment of
the policy
The basic building blocks and tools of their adaptive policy-making approach include the following.
P Basic policy – one or more options and plans for implementation
P Vulnerabilities – potential adverse consequences associated with the policy or side effects of the
policy
P Mitigating actions and hedging actions taken in advance to reduce risk of certain and possible
adverse effects of a policy
P Signposts – information that should be tracked in order to determine whether defensive or correc-
tive actions or a policy reassessment is needed
P Triggers – critical values of the signpost variables that lead to implementation of defensive or
corrective actions or to a policy reassessment
P Defensive actions taken after the fact to preserve a policy’s benefits, corrective actions to adjust
the basic policy in response to triggers, or reassessment when the policy has lost validity
Figure 2.3 Adaptive policy-making framework proposed by Walker, Rahman, and Cave (2001)
20 IISD-TERI-IDRC Adaptive Policies Project
The terminology of adaptive policy makes an appearance in the US National Academy of Science
literature in 2002 in relation to agent-based modelling. Bankes (2002) proposed refinements in agent-
based modelling approaches in recognition that ‘most policy problems involve complex and adaptive
systems, and that for those problems the classical approaches of predictive modelling and optimiza-
tion that have been used in decision-support software are not appropriate’. Bankes contends that for
policies to be successful in a complex and adaptive world, they will ‘need to be adaptive themselves’,
and warns that relying on optimization techniques to develop policies based on the projections of a
single model will produce static policies which make the ‘correct move’ for the best estimate model.
Alternatively, he believes ‘adaptive policies’ need to be evaluated on their ‘robustness’ properties, not
on their performance on any single case. Used properly, Bankes suggests that computers can be used
to ‘discover policies that are robust across multiple scenarios or alternative models, and to identify
and graphically depict sets of policies with satisfactory robustness.’ He concludes that computers can
be used to
‘… find important scenarios by searching through such ensembles, in particular to find cases that
break a proposed policy. Such worst cases can stimulate users to modify the range of possible policies
to allow for combinations that hedge against these possibilities. This strategy can allow users to iterate
with the computer to gradually evolve policy schemas that have particular policy instances with
desirable properties. This approach has been successfully used in several studies to make concrete
policy recommendations for deeply uncertain problems by using very non-linear simulations including
agent based.’
Ruitenbeek and Cartier (2001) make an indirect connection to adaptive policies in presenting a
continuum of policy instruments in terms of flexibility and government involvement, and discussing
them in relation to complex adaptive systems. On one end of the continuum are instruments with
minimum flexibility, maximum government involvement, and those described as control-oriented.
On the other end is maximum flexibility, increased private initiative and litigation-oriented instru-
ments. In between lie the market-oriented instruments. The authors note that virtually any instrument
along this continuum could be appropriate in a complex adaptive system, but that this would depend
on the nature of the system. For example, ‘if functioning social institutions are in place, decentralized
instruments requiring little government involvement may be a good policy choice. Conversely, impos-
ing strong external regulations within such a context could disrupt any positive natural evolution that
might occur.’
2.2.2 Insights from adaptive management literature
Insights from the literature on adaptive management are relevant for adaptive policies for two related
reasons. First, the two concepts are inherently similar in sharing the term ‘adaptive’ and therefore,
insights will be important for defining what adaptive policies are. Second, the two concepts are differ-
ent as ‘adaptive management’ deals with management of a broader set of policies directed at an issue,
while ‘adaptive policies’ deal more with individual policies or instruments.
The notion of adaptive management, as it applies to the process of human intervention in ecologi-
cal systems, is first attributed to the Canadian ecologist, Holling (1978). Holling describes adaptive
management as ‘learning by judicious doing’, and differs fundamentally from traditional anticipatory
management by acknowledging that policy is necessarily experimental. Adaptive management is
characterized by its flexible policies and the plurality of views that inform it; no particular epistemic
community can possess all the necessary knowledge to form policy. Science, models, expert knowl-
edge, and the policies based on them are not interpreted as ultimate answers, but merely as a means to
guide a cautious process of intervention in complex ecosystems. The goal of management shifts from
achieving a single target to an integrated view of maintaining ecosystem resilience, avoiding
for example, catastrophic and irreversible ‘flips’ to other equilibrium states (Holling 2001).
An early analysis of adaptive policy-making in a natural resource management context is provided
by Walters (1986). He discusses the problems that people and their institutions encounter in manag-
ing things like stocks of fish in the Great Lakes, and the damage caused by acid rain in Europe. Walters
first discusses the fact that we do not understand the complex natural systems and thus, are unable to
make good predictions as to the results of various policy measures. However, people are very reluctant
21Initial conceptual framework and literature review for understanding adaptive policies
to adopt the type of policy response that will work well in such a situation, namely an adaptive re-
sponse. It is very hard to get people to accept the idea of trying some responses to see how well they
work, rather than relying on analysis and prediction.
‘It is quite natural for most people to think about other large investment programmes in terms
of a careful sequence of tests using such devices as market surveys and pilot studies. Somehow it is
viewed as unscientific or threatening to talk about experimentation on large spatial scales, as though
experiments were things to be done only in boxes or on benches in university laboratories. Worse,
some scientists involved in our discussions were worried about the very notion of publicly admitting
uncertainty, and felt that it was important to maintain at least the appearance of consensus within the
scientific community.’ (Walters 1986, p. 343)
Walters goes on to discuss some approaches that natural resource managers can use to try to get
policy-makers to change their attitudes. His suggestions regarding the types of attitudes to promote
are listed in Table 1.
Walters’ advice on how to get people out of their normal analytical box is to highlight the difficult
trade-offs, so that the managers are forced to confront the difficulties rather than defer tough deci-
sions in the hope of improvements next year. Only when a stark reality is accepted, he feels, will
creativity and openness to new solutions be available. And in complex resource management situa-
tions, creativity is necessary if good solutions are to be found.
Stienemann (2003) sees the current trend towards this concept of adaptive management as
exploring three core principles.
P Experimentalism Adaptive managers emphasize experimentalism within a dynamic system,
recognizing that an ongoing search for knowledge is necessary to set and achieve goals.
P Multi-scalar analysis Adaptive managers model and monitor natural systems on multiple scales
of space and time
P Place sensitivity Adaptive managers adopt local places, understood as humanly occupied geo-
graphic places, as the perspective from which multi-scalar management orientates.
It is also her hypothesis, based on decades of experience of systematic weaknesses in environmen-
tal impact assessment processes, that if the adaptive management processes being proposed today are
to be successful, they will require new ways of involving the public in decision-making.
The differences between adaptive management and adaptive policy-making can perhaps become
blurred, particularly if one is dealing with an expenditure policy instrument in the form of a targeted
government project or programme. Busenburg (2001) helps somewhat in elucidating the difference
between adaptive management and adaptive policy by noting that an ‘adaptive management strategy
might include a number of parallel policy experiments designed to test different policy measures, as
well as procedures for measuring and communicating the results’. This was certainly the way adaptive
management was viewed in Kai Lee’s experience in the Columbia River Basin on the issue of salmon
restoration and hydropower development.
Table 2.2 Conventional vs adaptive attitudes about the objectives of formal policy analysis
Conventional Adaptive
Seek precise predictions Uncover range of possibilitiesBuild prediction from detailed understanding Predict from experience with aggregate responsesPromote scientific consensus Embrace alternativesMinimize conflict among actors Highlight difficult trade-offsEmphasize short-term objectives Promote long-term objectivesPresume certainty in seeking best action Evaluate future feedback and learningDefine best actions from a set of obvious alternatives Seek imagination in new optionsSeek productive equilibrium Expect and profit from change
Source Walters (1986, p. 351)
22 IISD-TERI-IDRC Adaptive Policies Project
Lee (1993) also introduces the notion of ‘civic science’ in his discussions of adaptive management
in the Columbia River Basin, which he describes as ‘being irreducibly public in the way responsibilities
are exercized, intrinsically technical, and open to learning from errors and profiting from success’.
He goes on to note that ‘the challenge of building and maintaining civic science, and the institutional
relations necessary to do civic science is at the individual level. This is because civic science is a politi-
cal activity; its spirit and value depend upon the players, who make up, modify, implement, and
perhaps subvert the rules’. Lee’s insights into adaptive management, adaptive policy, and particularly
his ideas on civic science highlight the importance of the human dimension of adaptive policies and
that learning from errors is a key aspect of the adaptive policy-making process.
2.2.3 Insights from policy pilot studies
The field of pilot studies can provide helpful insights for adaptive policies because pilot studies are
primarily mechanisms for learning and adaptation. A recent review conducted by the Cabinet Office
in the United Kingdom (UK 2003) focused on the role of pilot studies in policy-making. The study
noted that ‘an important innovation in recent years has been the phased introduction of major govern-
ment policies or programmes, allowing them to be tested, evaluated, and adjusted where necessary,
before being rolled out nationally’ (UK 2003, p. 3). The study noted that the practice of policy pilots
has been relatively widespread in the US owing in part to its federal structure, which allows state
policy-making to be regarded as large-scale experiments.
Among the recommendations made in the UK study, three, in particular, are relevant to adaptive
policies and policy-making in the context of this project. The first is, ‘a pilot should be undertaken in
the spirit of experimentation. If it is clear at the outset that a new policy and its delivery mechanisms
are effectively already cast in stone, a pilot is redundant and ought not to be undertaken’. The notion
of experimentation relates to the notion of adaptive policy as articulated by Lee (1993), and acknowl-
edges that uncertainty and surprise are inherent in the process. However, the policy pilot insights
appear to imply that once the experiment has been run, a guiding principle will emerge to ensure its
predictability. While this may be the case in many pure sciences, it is not the case in complex socio-
ecologic systems, which are adaptive. As Lee points out, it is ongoing policy development and experi-
mentation that is truly adaptive.
A second recommendation from the study deals with extending the notion of piloting beyond just
an initial stage to ‘a continuous processes of accumulating policy-relevant evidence’. A third recom-
mendation of the policy pilots study is that ‘appropriate mechanisms should always be in place to
adapt (or abandon) a policy or its delivery mechanism in light of a pilot’s findings’. Both of these
recommendations speak directly to the primary thrust of the notion of adaptive policy-making pre-
sented previously by Walker, Rahman, and Cave (2001), which was for learning and adaptation of the
policy to be made ‘explicit at the outset’ and the inevitable ‘policy changes to become part of a larger,
recognized process and not forced to be made repeatedly on an ad hoc basis’. So while the policy pilots
are an important initial stage in the life of a policy, the study concludes by making a call for the basic
premise of testing, learning, and adapting to become part of the ongoing policy life cycle.
The policy pilots study identified two types of pilots, which provide useful examples for our
project. These include the following.
P Impact pilot – tests of the likely effects of new policies, measuring or assessing their early out-
comes. They enable evidence of the effects of a policy change to be tested against a genuine coun-
terfactual, such as is provided by the use of control groups in a medical trial.
P Process pilots – designed to explore the practicalities of implementing a policy in a particular way
or a particular route, assessing what methods of delivery work best or are most cost-effective (UK
2003).
These two types simply provide an important reminder of incorporating both outcome- and
process-based aspects in assessing the performance of policies.
23Initial conceptual framework and literature review for understanding adaptive policies
2.2.4 Insights from the policy learning and change literature
A large literature on learning for policy change seems to conclude mostly that this is a complex and
indeterminate process, conditioned by the nature of the structures and processes involved, and by the
ways in which knowledge permeates these structures through interaction of individuals and groups.
There is widespread agreement on the importance of networks, coalitions, or communities of interest,
and the ways in which they interact, in the process of policy learning (Lindquist 2001; Sabatier 1999;
Stone 2001). But the role of new knowledge in affecting policy is much less clear. The process has been
studied in more detail with regard to new scientific knowledge. This is often debated and subject to
criticism, from within the framework of its disciplinary and scientific origins, and also by non-scien-
tific sceptics who are threatened by its implications. It takes some time, and occasionally a high-profile
public crisis, before scientific evidence attains a degree of ‘consensus’ in decision-making circles (Haas
1992).
Policy learning comes from a variety of sources: interestingly, academic research is a very limited
source of policy learning. It is widely agreed that despite all the resources devoted to social science and
policy research in the US during the 1960s and 1970s, there is very little evidence that it contributed
directly to measurable improvements in policy (Lindquist 2001). This ought to be particularly sobering
for those who emphasize the role of scientific rationality and analysis in policy-making.
How is knowledge used to create policy learning? The policy learning and change literature sug-
gests that new knowledge is always filtered by actors’ values and belief systems, prior experience,
association, relative power, professional training, and norms. Policy change is normally driven by
interactions among groups (or coalitions) of policy actors, where each group may include policy-
makers, researchers, business or professional interests, and advocates. These advocacy coalitions
compete with each other for power and political authority. Learning within these groups, like Haas’s
‘adaptation’, is normally a shallow process, limited to insights about choice of means and power
strategies (Bennett and Howlett 1992). Fundamental challenges to assumptions or core beliefs of such
groups are rare, partly because evidence is filtered by the group’s own processes of information ex-
change and validation. And deeply held values, which motivate and give meaning to individual policy
actors are highly resistant to learning (changes here are akin to religious conversion). However,
political actors who are related to, but not captured by, the coalition group can sometimes learn from
the discourse and debate between advocacy coalitions, and change their views on specific policy
actions (Sabatier and Jenkins-Smith 1999).
According to Haas, a key role in policy learning is played by ‘epistemic communities’: groups of
professionals who share normative beliefs which provide a value basis for social action; commitment
to a common causal model derived from study and analysis of a common set of problems or policy
linkages; shared notions of validity in their domain of expertise, and a common set of political values
and commitments to translate their perceived truths into policy (Haas 1992). At times of crisis or rapid
change, when information is at a premium, epistemic communities can become more important and
influential in the policy process. They can shed light on causal relations which had previously been
unsuspected, quantify uncertainties for decision-makers, help re-define the interests of the state or of
various political interests within it, and directly contribute to policy formulation (for example, through
framing alternatives). But most of the time, epistemic communities and technical expertise will play
only a limited role in policy formulation (Haas 1992).
Ultimately, the mechanisms by which even scientific consensus by epistemic communities can
influence policy are quite murky. The same specialists may provide the same consensual knowledge
to several governments, with quite diverse policy responses (for example, Haas points out that similar
evidence on environmental toxicity of specific chemicals nevertheless led to different regulatory
responses by government in Canada, the US, and Europe). What is clear is that fundamental changes
in underlying policy beliefs and assumptions, of the kind, which would probably be needed in the
event of policy failure or policy gaps due to external dynamics, are rare. Neither is knowledge neutral.
There are few areas of policy importance, which are not subject to scientific and technical debate,
24 IISD-TERI-IDRC Adaptive Policies Project
discourses between competing worldviews, and ‘mobilization of bias’ from available evidence
(Stone 2001).
There remains concern in the policy learning and change literature about the influence of techni-
cal specialists and their instrumentalist rationality on fundamental social and political decision-
making. By virtue of their expertise and scientific insights, technical specialists can have privileged
access to policy decision-makers. This is not always in the interest of adaptive policies if the key issues
in decision-making, and in learning, are issues of value and social change. Public debate and social
discourse are important tools to balance this privileged access of technical expertise to power (see
Steinemann 2003). Participatory processes also offer opportunities for policy learning, in ways,
which differ from the learning models described above driven by expert, elite, or advocacy networks.
The outcome of deliberative practice (that is, public decision-making which involves shared discourse,
deliberation, and social learning) is not abstract generalization, or discrete policy decisions, but shared
meaning by the participants, and engaging narrative accounts of success or failure in their own terms
(Forester 1999). Participatory processes are not merely about being heard, or about negotiation, or
about sharing evidence and building consensus on facts (although all these are important), but cru-
cially about political identity, about values, about building social cohesion and competence,
mutual respect, hope, and capacity to act. Such processes, though time-consuming, have crucial
transformative potential in creating new, shared vision, which can motivate learning and policy
adaptation.
Policy, whether ‘adaptive’ or not, is almost always modified in its implementation (Majone and
Wildawsky 1978). Policy ideals conceived as an analytical interpretation of complex problems, or as
negotiated agreement between conflicting power groups, must inevitably take shape through the
actions of implementing agents (typically lower-level administrators). This process almost always
allows discretion for substantial further political negotiation, interpretation, and modification as the
policy is put into practice (Sabatier and Jenkins-Smith 1999). How ought we to conceive of this imple-
mentation process in relation to policy adaptation? What aspects of policy implementation are of
interest to adaptive policy-making?
At the limits, policy implementers can act to deliberately subvert the original intent of the policy.
This kind of ‘adaptation’ is not constructive: it denies the purpose of policy-making and the role of
political accountability. Because policy differentially affects the interests of divergent groups, power
also comes into play in steering implementation. However, there is an important role for implement-
ing agents to play in smoothing the connection between the necessarily abstract and generalized views
of higher-level policy decision-making, and the frequently complex contexts of specific application.
If policy implementation is challenged legally, questions of interpretation can be resolved by the
courts (which often leads to policy revision or clarification). Most of the time, it will be public adminis-
trators and enforcement agents who are called on to interpret and enact policy. They use information,
judgment, precedent, and political power to introduce and sometimes negotiate modifications to
policy, which make it more easily implemented (Najam 1995).
Adaptive policy embraces the constructive and judicious interventions of administrative practi-
tioners who share the vision and goals of the policy itself. An important implication of this is that
effort needs to be devoted to building shared ownership of the policy vision and goals, best done
through consultation prior to policy approval, through the institutional instruments discussed earlier.
Policies intended to enable local responses to national issues often do not recognize the diversity
of contexts and conditions in which they will be applied. Through consultation, rapid iterations, or
‘policy trials’ the scope of these contexts can be explored, and policies revised accordingly. Adaptive
policies will use such opportunities to increase flexibility and ease of implementation through modifi-
cation. Adaptive policy-making will build in consultation and learning mechanisms, seeking practical
examples and counter-examples of implementation issues in the field, and using evidence from case
experiences to modify implementation frameworks. Monitoring and evaluation feedback are elements
of such learning systems (Tyler and Mallee 2006) on how participatory action research provides
helpful insights for this process.
Modification and adaptation of policies in their implementation may ultimately fail, or new
contexts may arise which are completely outside the effective domain of existing policy. Policies may
25Initial conceptual framework and literature review for understanding adaptive policies
sometimes need to be completely overhauled in the face of changing external conditions. Therefore,
beyond smoothing the implementation of policies, an adaptive policy system must facilitate policy
learning or change. This process is unlikely to be smooth and simple, and it will often be time-consum-
ing. But to facilitate adaptive policies, we should want to ensure that policy learning and change
eventually generate the desired outcome: effective adaptation to dynamic conditions.
But outcomes of learning in public policy may be difficult to specify. Adoption or non-adoption
of particular reforms or instruments are only fragmentary measures of the ways in which ideas,
lessons from elsewhere, and experience can contribute to changes in perceived roles, responsibilities,
and potential actions by various social and political actors. Over time, learning is coupled to broader
social processes, affecting value shifts that may lead to fundamental political changes. Policy learning
processes, if they are effective and conducted at least partly in the public eye, may lead to changes in
the perceptions of government and private roles (for example, in terms of responsibility for environ-
mental protection). And public learning may lead directly to change in political parties and govern-
ments, through the electoral process. Therefore, we can see that policy learning processes that enable
the policy system to become more adaptive also link to public and social learning, and to institutional
change.
2.2.5 Insights from the institutional learning literature
Berkes and Folke (2001) in their study of ecosystem dynamics and local knowledge define institutions
as ‘humanly devised constraints that structure human interaction. They are made up of formal con-
straints (rules, laws, constitutions), informal constraints (norms of behaviour, conventions, and self-
imposed codes of conduct), and their enforcement characteristics (North 1994)’. Berkes and Folke also
cite institutions as ‘the set of rules actually used by a set of individuals to organize repetitive activities
that produce outcomes affecting those individuals and potentially affecting others (Ostrom 1992)’.
In citing this definition, Berkes and Folke (2001) highlight that ‘institutions are socially con-
structed; they have normative and cognitive, as well as regulative dimensions’ (Scott 1995; Jentoff,
Colwell, Dresselhaus, et al. 1998). It is the cognitive dimension that Berkes and Folke (2001) focus on
in their study of ecosystem dynamics and local knowledge, because it is the cognitive dimension that
deals with questions of ‘the nature of knowledge and the legitimacy of different kinds of knowledge’.
Important to their work is the notion of institutional learning, which they note takes place at the
level of the institution as opposed to an individual level (Lee 1993). In relation to natural resources,
they describe institutional memory as memory of experience ‘which provides context for modification
of resource-use rules, regimes, and typically refers to a decadal scale of time’. It is noted that institu-
tional memory incorporated local or traditional knowledge, and it is this ‘knowledge and an under-
standing of how to respond to environmental change’ that are the ‘prerequisites for the management
and sustainable use of resources, biological diversity, and ecosystems (Berkes and Folke 2001)’.
They describe a conceptual framework for the analysis of linked social–ecological systems
(Figure 2.4). On the one side is a nested set of ecosystems while on the other is a nested set of man-
agement practices, which are embedded in a nested set of institutions. The linkage between the two
is provided by ecological knowledge and understanding, without which the likelihood of sustainable
natural resource use is ‘severely reduced’.
Haas (1990) identifies ‘adaptation’ in large international organizations as strategic behaviour,
which attempts to preserve the goals, identity, and boundaries of the organization in response to
stress, but to adjust its operational practices to ensure political survival. Adaptation is always incre-
mental, and does not involve fundamentally new knowledge or challenges to the organization’s as-
sumptions or ends. He distinguishes this from ‘learning’, which is much less frequent, and involves
the application of new consensual knowledge ‘to specify causal relations in new ways so the result
affects public policy’. Learning challenges individuals and organizations to question their fundamental
beliefs about cause and effect, which underlie organizational assumptions and goals. Overcoming and
changing behavioural patterns that led to past failure are central to Haas’ conception of policy learn-
ing. In this paper, we use the word ‘adaptation’ in different ways, but the concept of organizational
learning for policy change which Haas articulates is close to what we intend by the term.
26 IISD-TERI-IDRC Adaptive Policies Project
Organizational learning models developed for the private sector may not be very useful in the
public sector, where bureaucratic structure and behaviour undermine many of the precepts of the
models (Common 2004). In many government organizations the distance between decision-making
and service delivery can be very large (both geographically and organizationally), complicating the
ability of central authorities to benefit from the experience of field agents. A special problem in gov-
ernment organizations is the contradiction between learning and control: in conditions of flux and
confusion, when learning ought to be prioritized, such organizations are more concerned with politics
and control (Coopey 1996). There are some examples in Canada and in the UK of governments setting
up high-level groups to facilitate policy learning and external information flow. These groups were
charged with looking outside the government itself to lessons from domestic think tanks, research
organizations, and other states, consistent with organizational learning prescriptions to strengthen
‘cross-boundary’ information flows. While these have indeed increased access to information, it is
difficult to find evidence of systematic impacts on either policy formation or the operation of the
bureaucracy (Lindquist 2001; Common 2004).
In their seminal book Panarchy: understanding transformations in human and natural systems,
Gunderson, Holling, and Peterson (2001) present a theory of adaptive change based on observations
of ecosystems. Figure 2.5 presents this adaptive cycle within which four phases are typically seen.
P Exploitation – initially a few pioneers exploiting a resource
P Conservation – a mature and complex community
P Release – a sequence of rapid transformation triggered by disturbance (the beginning of a de-
crease in potential and adaptive capacity)
P Reorganization – a period of recovery leading to a decrease in potential and an increase in
connectedness that allows for another cycle of exploitation
Gunderson, Holling, and Peterson (2001) extend this four-phase cycle into the resource manage-
ment policy realm for purposes of linking ecological and social dynamics, and this provides a useful
perspective for adaptive policy-making. In their understanding, the four phases of the adaptive cycle
correspond to four phases of policy-making namely
P Exploitation: Policy plan
P Conservation: Policy implementation
P Release: Policy failure
P Reorganization: Policy alternatives
Figure 2.4 Conceptual framework for the analysis of linked social–ecological systems
Source Berkes and Folke (2001)
27Initial conceptual framework and literature review for understanding adaptive policies
From an institutional perspective, Gunderson, Holling, and Peterson (2001) note that the reor-
ganization phase (for example, policy alternatives) occurs when a ‘rare and unexpected intervention
or event can shape new futures as an act of creating opportunity’. In the conservation phase, tight
organization and hierarchical control, which precludes alternatives, is broken down due to the combi-
nation of maturing brittleness and external events. This ‘loss of control’ releases capital such as
money, skills, and experience and dissociation into constitutive elements. The authors note that it is at
this point that the system becomes ill defined and loosely coupled providing the conditions for either
collapse or innovation. It is at this stage where, particularly in human systems, the potential to influ-
ence the future is considered greatest.
Janssen (2001) describes a conceptual understanding of institutions which assumes that agents
change their preferred management style ‘if observations about the world are surprising enough—that
is, if observations differ enough from what the agents expect based on their worldview’ (Thompson
and Wildawsky 1990). They use the adaptive cycle and policy-making context presented previously
in Figure 3 to articulate the changes and adaptations of institutions. The description is as follows.
‘The [exploitation] phase is defined as policy formulation. If that policy is successful it
leads to increasing bureaucratic processes to formalize and institutionalize policies. The
expectations of the institutions are mainly based on insights and information during the
time policies were formulated. Since policy was considered to be successful, no new
investigation is done on the quality of the expectations. Those groups with other perspec-
tives on reality, leading to other expectations and preferred policies, will challenge ruling
institutions. In the event of a surprise, the ruling institution is confronted with evidence
that its expectations do not hold anymore, which can result in a crisis. Such surprises can
be natural disasters, scientific or technological revolutions, and so on. After the start of
such a crisis, a period will begin in which various alternative policies react to surprise.
This can lead to continuation of the ruling type of institution with new policy initiatives,
or a flip to a new type of institution (Janssen 2001, p. 250).’
Manley, Tracy, Murphy, et al. (2000) use the same framework, but develop a more applied cycle of
four phases of adaptive management for natural resources (Figure 2.6; p. 692).
P Information needs identification
P Information acquisition and assessment
P Evaluation and decision-making
P Management action
Figure 2.5 The adaptive cycle
Source Gunderson, Holling, and Peterson (2001)
28 IISD-TERI-IDRC Adaptive Policies Project
Their diagram of information and decision flow is much more applied compared to the conceptual
approach found in Gunderson, Holling, and Peterson (2001).
2.2.6 Insights from natural systems
Natural systems provide insights into what policy adaptation might be. Like policy systems, adaptive
natural systems interact with their environments. They respond in non-linear ways to changing
conditions. They are purposeful, in the sense that adaptation and transformation serve the perpetua-
tion and function of the whole system, not of individual components.
However, the last few decades have seen advances in physics, mathematics, and life sciences,
which have completely transformed scientists’ understanding of how nature works. At its roots, the
universe appears to be not composed of ‘objects’ at all, but of events and relationships, ephemeral
patterns of interaction which are impossible to predict or define except in probabilistic terms
(Zukav 1979). Living organisms appear to interact with many elements of their environments, both
material and non-material, in ways, which may be frequently undetectable and unpredictable. Tiny,
arbitrarily small changes seem capable, under certain conditions, of spawning large systemic conse-
quences. There is growing recognition that when we try to ensure order, structure, stability, and
certainty in our organizations and systems through policy interventions, we are trying to create un-
natural conditions. We attempt to plan and control an objective reality which may be illusory, and we
treat environmental systems and human organizations as responding predictably to change when
science tells us non-linear systems may be well-ordered but are essentially unpredictable (much of the
following section from the arguments of Wheatley 1999).
We have learned that highly adaptive natural systems have certain characteristics that reflect this
emerging scientific view. They are driven and structured by flows of information and energy and so
must remain open and responsive. Negative feedback uses information to provide control functions
for system elements and maintains dynamic equilibrium conditions. But adaptive natural systems also
swing into disequilibrium under unpredictable conditions, which then leads to rapid degradation of
system elements and their re-ordering in a transformed structure to preserve the original function of
the system. Particular attention is needed to what in systems terminology are called positive feedback
Figure 2.6 Four phases of adaptive management
Source Manley, Tracy, Murphy, et al. (2000)
29Initial conceptual framework and literature review for understanding adaptive policies
loops, which signal imminent transformative pressures. Positive feedback is when a change in one
direction causes the system to respond in ways, which strengthen that change (for example, melting
of Arctic sea ice which reduces surface albedo causing further local temperature increases). Positive
feedback signals that systems are about to move beyond incremental change to a chaotic transforma-
tion, which results in fundamental restructuring.
Policies also typically use control and regulatory mechanisms to maintain socio-economic systems
through negative feedback. However, most information flows in large public or private organizations
are designed to provide measures of how well programmes are going and why things are working out
as expected. Adaptive policies should recognize limits to control, and seek instead to foster attention
to the unexpected, the counter-intuitive, and the changes in elements, which cannot be controlled.
They should address qualitative change, and pay more attention to fundamental goals and values,
which are long-lived measures, in addition to quantitative indicators, which may mask, rather than
clarify, meaning. Policy design and targets should not focus solely on component elements, but also
give attention to the broader whole, the big picture.
As science demonstrates that the foundational and persistent elements of our world are not
objects or structures but forces and relationships, so adaptive policies need to address dynamic inter-
actions between organizations, people, and the world around them. Just as very complex natural
structures can be seen to be built out of simple repeated patterns, which interact at different scales,
so adaptive policies might have simple and scalable principles, which respond to complex situations
interactively rather than prescriptively.
Adaptive policies will enable and encourage positive action. Adaptive responses in large-scale
socio-economic systems come from the creative action and engagement of people with their environ-
ment, not from their isolation and control. Policies that foster participation and encourage exchange
of information will engage multiple actors in processes of change more quickly than otherwise.
Finally, natural science teaches us that healthy outcomes for adaptive systems come from simple,
well-designed, and iterative ‘processes’, not from strong structures. Adaptive policies should be proc-
ess-oriented.
2.2.7 Insights from complex adaptive systems theory
In the policy and management fields, a substantive body of knowledge has emerged over the last 10
years on the topic of complex adaptive systems. A complex adaptive system is a conceptual articulation
of the real world and has been described as being…
‘…made up of many individual, “self-organizing elements” capable of responding to others and to
their environment. The entire system can be seen as a “network of relationships and interactions”, in
which the whole is very much more than the sum of the parts. “A change in any part of the system”,
even in a single element, “produces reactions and changes in associated elements” and the environ-
ment. Therefore, the effects of any one intervention in the “system cannot be predicted with complete
accuracy”, because the “system is always responding and adapting” to changes and the actions of
individuals, (Glouberman, Campsie, Gemar, et al. 2003).
The study of complex adaptive systems has been predicated by a need to better see the structures
underlying complex situations and identifying the best leverage points for change. As illustrated in
Table 2.3, the recent study and application of complex adaptive systems can be seen in numerous
fields including business management, health care, information technology, transportation, sustain-
able development, and international development.
Some interesting applications of complex systems thinking were seen in the early 1990s in busi-
ness management. Peter Senge’s The Fifth Discipline – the fifth discipline being systems thinking –
was on the bestseller list in 1993 and delivered pragmatic guidance on how to create and foster a
learning organization (Senge 1993). About the same time, Margaret J Wheatley drew on chaos and
complexity theory to provide advice for a simpler way to lead organizations (Wheatley 1999). A recent
search of the Internet turned up an investment fund management firm which bases its portfolio
strategy on an understanding and appreciation of complex adaptive systems (Innovative Portfolio
Strategies 2004).
30 IISD-TERI-IDRC Adaptive Policies Project
Complex adaptive systems theory is beginning to influence many facets of public policy and
management. Perhaps most active is the research and application in the health care field. The Mayo
Clinic in the US has sponsored recent conferences on the topic, and a book has been published synthe-
sizing the outcomes of a symposium held in Florida. More recently, the Caledon Institute for Social
Policy in Canada has created a toolbox for improving health in cities based explicitly on the realization
that the cities behave as complex adaptive systems (Box 2.5).
The information technology sector published a seminal piece in 2000 called Harnessing Complex-
ity: organizational implications for a scientific frontier (Axelrod and Cohen 2000). Funded by the US
Department of Defence, this research project constructed a theoretical framework of complex adaptive
systems with the purpose of understanding how the theory could be harnessed for policy related to the
Table 2.3 Scan of theoretical and applied complex adaptive systems research in business and public policy
Sector
Business managementOrganizational learningLeadershipInvestment fund management
Health policyHealth policy in citiesHealthcare and healthcareadministration
Information policyBottom-up management related toinformation technology
Transportation policyAirport design
Sustainable developmentTheory of adaptive changeCommunity-level resilience buildingSustainability assessmentForest managementInternational developmentguidance for developmentassistance
Practitioner networksEcosystem management
Network of excellence
Application reference
Senge (1993)Wheatley (1999)Innovative Portfolio Strategies (2004)
Glouberman, Campsie, Gemar, et al. (2003)Mayo Clinic (2003)
Axelrod and Cohen (2000)
Walker, Rahman, and Cave (2001)
Gunderson and Holling (2002)Berkes, Colding, and Folke (2003)Kay, Regier, and Francis (1999) Ruitenbeek and Cartier (2001)
Rihani (2002)
The Resilience Alliance is a multidisciplinary research group thatexplores the dynamics of complex adaptive systems in order to discoverfoundations for sustainability, and provide novel solutions to managingresilience and coping with change, uncertainty, and surprise in complexsocial–ecological systems.www.resalliance.org
EU Complex systems network of excellence. Funded by the EuropeanCommission to develop collaboration among European researchersinterested in Complex Systems, from fundamental concepts toapplications, and involving academia, business, and industryhttp://www.complexityscience.org/index.php
31Initial conceptual framework and literature review for understanding adaptive policies
Internet information technology wave. This theoretical framework has been cited in many other policy
applications including health policy (Glouberman, Campsie, Gemar, et al. 2003) and forestry manage-
ment (Ruitenbeek and Cartier 2001).
About the same time that the BACH framework was being developed, another group of researchers
represented by the Resilience Alliance5 was doing similar work. This was a three-year project funded
by the MacArthur Foundation to advance the theory, policy, and practice involved in resolving issues
that emerge from the interaction of people and nature. The pinnacle report from this project was a
book entitled Panarchy (Gunderson and Holling 2002). Panarchy is about a quest for a theory of
adaptive change in integrated systems of humans and nature that will integrate across space from
local to global, across time from months to millennia, and across disciplines to understand systems of
linked ecological, economic, and institutional processes. A research network is active on this topic and
has taken on the objective of exploring the dynamics of complex adaptive systems in order to discover
foundations for sustainability and provide novel solutions to managing resilience and coping with
change, uncertainty, and surprise in complex social–ecological systems.
Advice for development policy-makers and project managers continues to flow out of the Panarchy
umbrella of understanding. For example, Berkes, Colding, and Folke (2003) have recently published a
book entitled Navigating social–ecological systems: building resilience for complexity and change,
which identifies a number of critical factors that seem to be required for dealing with nature’s dynam-
ics in social–ecological systems. Like Panarchy, the work of James Kay at the University of Waterloo
on Self-Organizing, Holarchic Open systems (Kay, Regier, Boyle, et al. 1999) has provided much
insight into complex adaptive systems.
Box
2.5
A toolbox for improving health in cities
P Gather information – Understand local conditions, strengths, and assets
P Respect history – ‘Adaptive systems are shaped by their past and a knowledge
of this history may suggest constraints on and opportunities on what can be
done in the future.’
P Consider interaction – Understand interactions with the natural, built, and
social environment
P Promote variation – ‘Introducing small-scale interventions for the same
problem offers greater hope of finding effective solutions.’ ‘It is critical to
understand and accept that many interventions will fail. Such failures should
not be viewed as failures of the overall way of understanding the system. This is
simply a feature of how one develops successful interventions in complex
adaptive systems.’
P Conduct selection – In complex adaptive systems possible solutions undergo
selection by the system. It is therefore important to include ‘evaluating
performance of potential solutions, and selecting the best candidates for further
support and development.’
P Fine-tune process – ‘In complex systems, which change over time and respond
dynamically to outside forces, it is necessary to constantly refine interventions
through a continual process of variation and selection.’
P Encourage self-organization – ‘Complex adaptive systems often spontaneously
generate solutions to problems without external input or formally organized
interventions.’ ‘This self-organizing capacity is a free good that can be valuable
in producing innovative and novel approaches to problems.’
Source Glouberman, Campsie, Gemar, et al. (2003)
5 <http://www.resalliance.org /ev.php>
32 IISD-TERI-IDRC Adaptive Policies Project
A recent book on complex adaptive systems and international development articulates a paradigm
shift that is based on notions of learning and adaptation (Rihani 2002). This publication describes the
following.
‘At base, development is what nations do as Complex Adaptive Systems, and what
they do can be described as uncertain evolution that has no beginning or end, nor
shortcuts, and few signposts on the way…prospects for the future are conditioned by two
factors that are difficult to predict and guard against in advance: local opportunities and
constraints, and the activities of other co-evolving nations.’
Based on this conceptual understanding of the world, Rihani provides some very relevant policy
guidance, guidance that is particularly insightful for understanding adaptive policies. He notes that
under the above conditions…
‘… rigid plans and policies are inappropriate…The only evolutionary stable strategy
open to a nation is to exercise flexibility and pragmatism in order to survive, learn, and
adapt over and over again in accordance with its ever-changing fitness landscape.
Critically, there is no evolution or progress without interactions; members of the popula-
tion have to be free and able to interact for anything to happen (Rihani 2002).’
Together, the findings from research focused on complex adaptive systems provide an insightful
set of principles for effective policy intervention in complex adaptive systems (for example, the real
world). In a 2003 workshop at the IISD, much of this literature was synthesized to create such a list of
principles. When organized according to a set of ideal policy-making steps, these principles provide
useful guidance to policy-makers towards how to build adaptability into policies (refer to Table 2.1).
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